OpenRaiser/Intern-Atlas huggingface dataset:
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Venue: arXiv cs.CL 2025
Year: 2025

We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and are spoken across various continents. The data instances are multi-labeled with six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) multilabel emotion detection, (b) emotion intensity score detection, and (c) cross-lingual emotion detection. The task attracted over 700 participants. We received final submissions from more than 200 teams and 93 system description papers. We report baseline results, along with findings on the best-performing systems, the most common approaches, and the most effective methods across different tracks and languages. The datasets for this task are publicly available. The dataset is available at SemEval2025 Task 11 https://brighter-dataset.github.io
Goal Conditioned Reinforcement Learning for Photo Finishing Tuning
Venue: arXiv cs.GR 2025
Year: 2025

Photo finishing tuning aims to automate the manual tuning process of the photo finishing pipeline, like Adobe Lightroom or Darktable. Previous works either use zeroth-order optimization, which is slow when the set of parameters increases, or rely on a differentiable proxy of the target finishing pipeline, which is hard to train. To overcome these challenges, we propose a novel goal-conditioned reinforcement learning framework for efficiently tuning parameters using a goal image as a condition. Unlike previous approaches, our tuning framework does not rely on any proxy and treats the photo finishing pipeline as a black box. Utilizing a trained reinforcement learning policy, it can efficiently find the desired set of parameters within just 10 queries, while optimization based approaches normally take 200 queries. Furthermore, our architecture utilizes a goal image to guide the iterative tuning of pipeline parameters, allowing for flexible conditioning on pixel-aligned target images, style images, or any other visually representable goals. We conduct detailed experiments on photo finishing tuning and photo stylization tuning tasks, demonstrating the advantages of our method. Project website: https://openimaginglab.github.io/RLPixTuner/.
The Influence Operation Ontology (IOO)
Venue: arXiv cs.SI 2025
Year: 2025

Ontologies provide a systematic framework for organizing and leveraging knowledge, enabling smarter and more effective decision-making. In order to advance in the capitalization and augmentation of intelligence related to nowadays cyberoperations, the proposed Influence Operation Ontology (IOO) establishes the main entities and relationships to model offensive tactics and techniques by threat actors against the public audience through the information environment. It aims to stimulate research and development in the field, leading to innovative applications against influence operations, particularly in the fields of intelligence, security, and defense.
A Decapod Robot with Rotary Bellows-Enclosed Soft Transmissions
Venue: arXiv cs.RO 2025
Year: 2025

Soft crawling robots exhibit efficient locomotion across various terrains and demonstrate robustness to diverse environmental conditions. Here, we propose a valveless soft-legged robot that integrates a pair of rotary bellows-enclosed soft transmission systems (R-BESTS). The proposed R-BESTS can directly transmit the servo rotation into leg swing motion. A timing belt controls the pair of R-BESTS to maintain synchronous rotation in opposite phases, realizing alternating tripod gaits of walking and turning. We explored several designs to understand the role of a reinforcement skeleton in twisting the R-BESTS' input bellows units. The bending sequences of the robot legs are controlled through structural design for the output bellows units. Finally, we demonstrate untethered locomotion with the soft robotic decapod. Experimental results show that our robot can walk at 1.75 centimeters per second (0.07 body length per second) for 90 min, turn with a 15-centimeter (0.6 BL) radius, carry a payload of 200 g, and adapt to different terrains.
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural network (NN) with millions or billions of parameters can perform really well on unseen data, after being trained from a finite training set. Various prior theories have been developed to explain such excellent ability of NNs, but do not provide a meaningful bound on the test error. Some recent theories, based on PAC-Bayes and mutual information, are non-vacuous and hence show a great potential to explain the excellent performance of NNs. However, they often require a stringent assumption and extensive modification (e.g. compression, quantization) to the trained model of interest. Therefore, those prior theories provide a guarantee for the modified versions only. In this paper, we propose two novel bounds on the test error of a model. Our bounds uses the training set only and require no modification to the model. Those bounds are verified on a large class of modern NNs, pretrained by Pytorch on the ImageNet dataset, and are non-vacuous. To the best of our knowledge, these are the first non-vacuous bounds at this large scale, without any modification to the pretrained models.
MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning
Venue: arXiv cs.CV 2025
Year: 2025

DeepSeek R1, and o1 have demonstrated powerful reasoning capabilities in the text domain through stable large-scale reinforcement learning. To enable broader applications, some works have attempted to transfer these capabilities to multimodal reasoning. However, these efforts have been limited by the limited difficulty of selected tasks and relatively small training scales, making it challenging to demonstrate strong multimodal reasoning abilities. To address this gap, we introduce the MMK12 dataset and MM-EUREKA with 7B and 32B parameters. The former is a high-quality multimodal mathematics reasoning dataset featuring diverse knowledge domains with human-verified answers and solution processes. The latter is a multimodal model employing rule-based reinforcement learning on MMK12, utilizing online filtering and two-stage training strategy to enhance training stability. MM-EUREKA demonstrates remarkable performance gains in multimodal mathematical reasoning, outperforming previous powerful models like InternVL2.5-78B or InternVL2.5-38B-MPO. In particular, MM-EUREKA achieves competitive or superior performance compared to both open-source and closed-source models, and trails slightly behind o1 in multidisciplinary reasoning tasks. We open-source our complete pipeline to foster further research in this area. We release all our codes, models, data, etc. at https://github.com/ModalMinds/MM-EUREKA
Are System Optimal Dynamic Flows Implementable by Tolls?
Venue: arXiv cs.GT 2025
Year: 2025

A seminal result of [Fleischer et al. and Karakostas and Kolliopulos, both FOCS 2004] states that system optimal multi-commodity static network flows are always implementable as tolled Wardrop equilibrium flows even if users have heterogeneous value-of-time sensitivities. Their proof uses LP-duality to characterize the general implementability of network flows by tolls. For the much more complex setting of $\textit{dynamic flows}$, [Graf et al., SODA 2025] identified necessary and sufficient conditions for a dynamic $s$-$d$ flow to be implementable as a tolled dynamic equilibrium. They used the machinery of (infinite-dimensional) strong duality to obtain their characterizations. Their work, however, does not answer the question of whether system optimal dynamic network flows are implementable by tolls. We consider this question for a general dynamic flow model involving multiple commodities with individual source-destination pairs, fixed inflow rates and heterogeneous valuations of travel time and money spent. We present both a positive and a, perhaps surprising, negative result: For the negative result, we provide a network with multiple source and destination pairs in which under the Vickrey queuing model no system optimal flow is implementable -- even if all users value travel times and spent money the same. Our counter-example even shows that the ratio of the achievable equilibrium travel times by using tolls and of the system optimal travel times can be unbounded. For the single-source, single-destination case, we show that if the traversal time functions are suitably well-behaved (as is the case, for example, in the Vickrey queuing model), any system optimal flow is implementable.
PER-DPP Sampling Framework and Its Application in Path Planning
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous navigation in intelligent mobile systems represents a core research focus within artificial intelligence-driven robotics. Contemporary path planning approaches face constraints in dynamic environmental responsiveness and multi-objective task scalability, limiting their capacity to address growing intelligent operation requirements. Decision-centric reinforcement learning frameworks, capitalizing on their unique strengths in adaptive environmental interaction and self-optimization, have gained prominence in advanced control system research. This investigation introduces methodological improvements to address sample homogeneity challenges in reinforcement learning experience replay mechanisms. By incorporating determinant point processes (DPP) for diversity assessment, we develop a dual-criteria sampling framework with adaptive selection protocols. This approach resolves representation bias in conventional prioritized experience replay (PER) systems while preserving algorithmic interoperability, offering improved decision optimization for dynamic operational scenarios. Key contributions comprise: Develop a hybrid sampling paradigm (PER-DPP) combining priority sequencing with diversity maximization.Based on this,create an integrated optimization scheme (PER-DPP-Elastic DQN) merging diversity-aware sampling with adaptive step-size regulation. Comparative simulations in 2D navigation scenarios demonstrate that the elastic step-size component temporarily delays initial convergence speed but synergistically enhances final-stage optimization with PER-DPP integration. The synthesized method generates navigation paths with optimized length efficiency and directional stability.
Creating and Evaluating Privacy and Security Micro-Lessons for Elementary School Children
Venue: arXiv cs.HC 2025
Year: 2025

The growing use of technology in K--8 classrooms highlights a parallel need for formal learning opportunities aimed at helping children use technology safely and protect their personal information. Even the youngest students are now using tablets, laptops, and apps to support their learning; however, there are limited curricular materials available for elementary and middle school children on digital privacy and security topics. To bridge this gap, we developed a series of micro-lessons to help K--8 children learn about digital privacy and security at school. We first conducted a formative study by interviewing elementary school teachers to identify the design needs for digital privacy and security lessons. We then developed micro-lessons -- multiple 15-20 minute activities designed to be easily inserted into the existing curriculum -- using a co-design approach with multiple rounds of developing and revising the micro-lessons in collaboration with teachers. Throughout the process, we conducted evaluation sessions where teachers implemented or reviewed the micro-lessons. Our study identifies strengths, challenges, and teachers' tailoring strategies when incorporating micro-lessons for K--8 digital privacy and security topics, providing design implications for facilitating learning about these topics in school classrooms.
Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds
Venue: arXiv cs.CV 2025
Year: 2025

The adoption of Millimeter-Wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of Open-set Gait Recognition (OSGR) from sparse mmWave radar point clouds. Unlike most existing research, which assumes a closed-set scenario, our work considers the more realistic open-set case, where unknown subjects might be present at inference time, and should be correctly recognized by the system. Point clouds are well-suited for edge computing applications with resource constraints, but are more significantly affected by noise and random fluctuations than other representations, like the more common micro-Doppler signature. This is the first work addressing open-set gait recognition with sparse point cloud data. To do so, we propose a novel neural network architecture that combines supervised classification with unsupervised reconstruction of the point clouds, creating a robust, rich, and highly regularized latent space of gait features. To detect unknown subjects at inference time, we introduce a probabilistic novelty detection algorithm that leverages the structured latent space and offers a tunable trade-off between inference speed and prediction accuracy. Along with this paper, we release mmGait10, an original human gait dataset featuring over five hours of measurements from ten subjects, under varied walking modalities. Extensive experimental results show that our solution attains F1-Score improvements by 24% over state-of-the-art methods adapted for point clouds, on average, and across multiple openness levels.
V2Flow: Unifying Visual Tokenization and Large Language Model Vocabularies for Autoregressive Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

We propose V2Flow, a novel tokenizer that produces discrete visual tokens capable of high-fidelity reconstruction, while ensuring structural and latent distribution alignment with the vocabulary space of large language models (LLMs). Leveraging this tight visual-vocabulary coupling, V2Flow enables autoregressive visual generation on top of existing LLMs. Our approach formulates visual tokenization as a flow-matching problem, aiming to learn a mapping from a standard normal prior to the continuous image distribution, conditioned on token sequences embedded within the LLMs vocabulary space. The effectiveness of V2Flow stems from two core designs. First, we propose a Visual Vocabulary resampler, which compresses visual data into compact token sequences, with each represented as a soft categorical distribution over LLM's vocabulary. This allows seamless integration of visual tokens into existing LLMs for autoregressive visual generation. Second, we present a masked autoregressive Rectified-Flow decoder, employing a masked transformer encoder-decoder to refine visual tokens into contextually enriched embeddings. These embeddings then condition a dedicated velocity field for precise reconstruction. Additionally, an autoregressive rectified-flow sampling strategy is incorporated, ensuring flexible sequence lengths while preserving competitive reconstruction quality. Extensive experiments show that V2Flow outperforms mainstream VQ-based tokenizers and facilitates autoregressive visual generation on top of existing. https://github.com/zhangguiwei610/V2Flow
PE3R: Perception-Efficient 3D Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in 2D-to-3D perception have significantly improved the understanding of 3D scenes from 2D images. However, existing methods face critical challenges, including limited generalization across scenes, suboptimal perception accuracy, and slow reconstruction speeds. To address these limitations, we propose Perception-Efficient 3D Reconstruction (PE3R), a novel framework designed to enhance both accuracy and efficiency. PE3R employs a feed-forward architecture to enable rapid 3D semantic field reconstruction. The framework demonstrates robust zero-shot generalization across diverse scenes and objects while significantly improving reconstruction speed. Extensive experiments on 2D-to-3D open-vocabulary segmentation and 3D reconstruction validate the effectiveness and versatility of PE3R. The framework achieves a minimum 9-fold speedup in 3D semantic field reconstruction, along with substantial gains in perception accuracy and reconstruction precision, setting new benchmarks in the field. The code is publicly available at: https://github.com/hujiecpp/PE3R.
Sometimes the Model doth Preach: Quantifying Religious Bias in Open LLMs through Demographic Analysis in Asian Nations
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.
Language Models Fail to Introspect About Their Knowledge of Language
Venue: arXiv cs.CL 2025
Year: 2025

There has been recent interest in whether large language models (LLMs) can introspect about their own internal states. Such abilities would make LLMs more interpretable, and also validate the use of standard introspective methods in linguistics to evaluate grammatical knowledge in models (e.g., asking "Is this sentence grammatical?"). We systematically investigate emergent introspection across 21 open-source LLMs, in two domains where introspection is of theoretical interest: grammatical knowledge and word prediction. Crucially, in both domains, a model's internal linguistic knowledge can be theoretically grounded in direct measurements of string probability. We then evaluate whether models' responses to metalinguistic prompts faithfully reflect their internal knowledge. We propose a new measure of introspection: the degree to which a model's prompted responses predict its own string probabilities, beyond what would be predicted by another model with nearly identical internal knowledge. While both metalinguistic prompting and probability comparisons lead to high task accuracy, we do not find evidence that LLMs have privileged "self-access". By using general tasks, controlling for model similarity, and evaluating a wide range of open-source models, we show that LLMs cannot introspect, and add new evidence to the argument that prompted responses should not be conflated with models' linguistic generalizations.
XIFBench: Evaluating Large Language Models on Multilingual Instruction Following
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications. However, their performance in multilingual settings lacks systematic investigation, with existing evaluations lacking fine-grained constraint analysis across diverse linguistic contexts. We introduce XIFBench, a comprehensive constraint-based benchmark for evaluating multilingual instruction-following abilities of LLMs, comprising 558 instructions with 0-5 additional constraints across five categories (Content, Style, Situation, Format, and Numerical) in six languages spanning different resource levels. To support reliable and consistent cross-lingual evaluation, we implement three methodological innovations: cultural accessibility annotation, constraint-level translation validation, and requirement-based evaluation using English requirements as semantic anchors across languages. Extensive experiments with various LLMs not only quantify performance disparities across resource levels but also provide detailed insights into how language resources, constraint categories, instruction complexity, and cultural specificity influence multilingual instruction-following. Our code and data are available at https://github.com/zhenyuli801/XIFBench.
Queueing, Predictions, and LLMs: Challenges and Open Problems
Venue: arXiv cs.AI 2025
Year: 2025

Queueing systems present many opportunities for applying machine-learning predictions, such as estimated service times, to improve system performance. This integration raises numerous open questions about how predictions can be effectively leveraged to improve scheduling decisions. Recent studies explore queues with predicted service times, typically aiming to minimize job time in the system. We review these works, highlight the effectiveness of predictions, and present open questions on queue performance. We then move to consider an important practical example of using predictions in scheduling, namely Large Language Model (LLM) systems, which presents novel scheduling challenges and highlights the potential for predictions to improve performance. In particular, we consider LLMs performing inference. Inference requests (jobs) in LLM systems are inherently complex; they have variable inference times, dynamic memory footprints that are constrained by key-value (KV) store memory limitations, and multiple possible preemption approaches that affect performance differently. We provide background on the important aspects of scheduling in LLM systems, and introduce new models and open problems that arise from them. We argue that there are significant opportunities for applying insights and analysis from queueing theory to scheduling in LLM systems.
KSOD: Knowledge Supplement for LLMs On Demand
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet still produce errors in domain-specific tasks. To further improve their performance, we propose KSOD (Knowledge Supplement for LLMs On Demand), a novel framework that empowers LLMs to improve their capabilities with knowledge-based supervised fine-tuning (SFT). KSOD analyzes the causes of errors from the perspective of knowledge deficiency by identifying potential missing knowledge in LLM that may lead to the errors. Subsequently, KSOD tunes a knowledge module on knowledge dataset and verifies whether the LLM lacks the identified knowledge based on it. If the knowledge is verified, KSOD supplements the LLM with the identified knowledge using the knowledge module. Tuning LLMs on specific knowledge instead of specific task decouples task and knowledge and our experiments on two domain-specific benchmarks and four general benchmarks empirically demonstrate that KSOD enhances the performance of LLMs on tasks requiring the supplemented knowledge while preserving their performance on other tasks. Our findings shed light on the potential of improving the capabilities of LLMs with knowledge-based SFT.